Megan A.K. Peters

Anodal tDCS to V1 blocks visual perceptual learning consolidation.

This study examined the effects of visual cortex transcranial direct current stimulation (tDCS) on visual processing and learning. Participants performed a contrast detection task on two consecutive days. Each session consisted of a baseline measurement followed by measurements made during active or sham stimulation. On the first day, one group received anodal stimulation to primary visual cortex (V1), while another received cathodal stimulation. Stimulation polarity was reversed for these groups on the second day. The third (control) group of subjects received sham stimulation on both days. No improvements or decrements in contrast sensitivity relative to the same-day baseline were observed during real tDCS, nor was any within-session learning trend observed. However, task performance improved significantly from Day 1 to Day 2 for the participants who received cathodal tDCS on Day 1 and for the sham group. No such improvement was found for the participants who received anodal stimulation on Day 1, indicating that anodal tDCS blocked overnight consolidation of visual learning, perhaps through engagement of inhibitory homeostatic plasticity mechanisms or alteration of the signal-to-noise ratio within stimulated cortex. These results show that applying tDCS to the visual cortex can modify consolidation of visual learning.

0 + 1 > 1: How adding noninformative sound improves performance on a visual task

It is well known that the nervous system combines information from different cues within and across sensory modalities to improve performance on perceptual tasks. In this article, we present results showing that in a visual motion-detection task, concurrent auditory motion stimuli improve accuracy even when they do not provide any useful information for the task. When participants judged which of two stimulus intervals contained visual coherent motion, the addition of identical moving sounds to both intervals improved accuracy. However, this enhancement occurred only with sounds that moved in the same direction as the visual motion. Therefore, it appears that the observed benefit of auditory stimulation is due to auditory-visual interactions at a sensory level. Thus, auditory and visual motion-processing pathways interact at a sensory-representation level in addition to the level at which perceptual estimates are combined.

The Size-Weight Illusion is not anti-Bayesian after all: a unifying Bayesian account

When we lift two differently-sized but equally-weighted objects, we expect the larger to be heavier, but the smaller feels heavier. However, traditional Bayesian approaches with “larger is heavier” priors predict the smaller object should feel lighter; this Size-Weight Illusion (SWI) has thus been labeled “anti-Bayesian” and has stymied psychologists for generations. We propose that previous Bayesian approaches neglect the brain’s inference process about density. In our Bayesian model, objects’ perceived heaviness relationship is based on both their size and inferred density relationship: observers evaluate competing, categorical hypotheses about objects’ relative densities, the inference about which is then used to produce the final estimate of weight. The model can qualitatively and quantitatively reproduce the SWI and explain other researchers’ findings, and also makes a novel prediction, which we confirmed. This same computational mechanism accounts for other multisensory phenomena and illusions; that the SWI follows the same process suggests that competitive-prior Bayesian inference can explain human perception across many domains.

Heuristic use of perceptual evidence leads to dissociation between performance and metacognitive sensitivity

Zylberberg et al. [Zylberberg, Barttfeld, & Sigman (Frontiers in Integrative Neuroscience, 6; 79, 2012), Frontiers in Integrative Neuroscience 6:79] found that confidence decisions, but not perceptual decisions, are insensitive to evidence against a selected perceptual choice. We present a signal detection theoretic model to formalize this insight, which gave rise to a counter-intuitive empirical prediction: that depending on the observer’s perceptual choice, increasing task performance can be associated with decreasing metacognitive sensitivity (i.e., the trial-by-trial correspondence between confidence and accuracy). The model also provides an explanation as to why metacognitive sensitivity tends to be less than optimal in actual subjects. These predictions were confirmed robustly in a psychophysics experiment. In a second experiment we found that, in at least some subjects, the effects were replicated even under performance feedback designed to encourage optimal behavior. However, some subjects did show improvement under feedback, suggesting the tendency to ignore evidence against a selected perceptual choice may be a heuristic adopted by the perceptual decision-making system, rather than reflecting inherent biological limitations. We present a Bayesian modeling framework that explains why this heuristic strategy may be advantageous in real-world contexts.

Who’s afraid of response bias?

Abstract Response bias (or criterion) contamination is insidious in studies of consciousness: that observers report they do not see a stimulus may not mean they have absolutely no subjective experience; they may be giving such reports in relative terms in the context of other stimuli. Bias-free signal detection theoretic measures provide an excellent method for avoiding response bias confounds, and many researchers correctly adopt this approach. However, here we discuss how a fixation on avoiding criterion effects can also be misleading and detrimental to fruitful inquiry. In a recent paper, Balsdon and Azzopardi (Absolute and relative blindsight. Consciousness and Cognition 2015; 32:79–91.) claimed that contamination by response bias led to flawed findings in a previous report of “relative blindsight”. We argue that their criticisms are unfounded. They mistakenly assumed that others were trying (and failing) to apply their preferred methods to remove bias, when there was no such intention. They also dismissed meaningful findings because of their dependence on criterion, but such dismissal is problematic: many real effects necessarily depend on criterion. Unfortunately, these issues are technically tedious, and we discuss how they may have confused others to misapply psychophysical metrics and to draw questionable conclusions about the nature of TMS (transcranial magnetic stimulation)-induced blindsight. We conclude by discussing the conceptual importance of criterion effects in studies of conscious awareness: we need to treat them carefully, but not to avoid them without thinking.

Smaller = Denser, and the Brain Knows It: Natural Statistics of Object Density Shape Weight Expectations

If one nondescript object’s volume is twice that of another, is it necessarily twice as heavy? As larger objects are typically heavier than smaller ones, one might assume humans use such heuristics in preparing to lift novel objects if other informative cues (e.g., material, previous lifts) are unavailable. However, it is also known that humans are sensitive to statistical properties of our environments, and that such sensitivity can bias perception. Here we asked whether statistical regularities in properties of liftable, everyday objects would bias human observers’ predictions about objects’ weight relationships. We developed state-of-the-art computer vision techniques to precisely measure the volume of everyday objects, and also measured their weight. We discovered that for liftable man-made objects, “twice as large” doesn’t mean “twice as heavy”: Smaller objects are typically denser, following a power function of volume. Interestingly, this “smaller is denser” relationship does not hold for natural or unliftable objects, suggesting some ideal density range for objects designed to be lifted. We then asked human observers to predict weight relationships between novel objects without lifting them; crucially, these weight predictions quantitatively match typical weight relationships shown by similarly-sized objects in everyday environments. These results indicate that the human brain represents the statistics of everyday objects and that this representation can be quantitatively abstracted and applied to novel objects. Finally, that the brain possesses and can use precise knowledge of the nonlinear association between size and weight carries important implications for implementation of forward models of motor control in artificial systems.

Volumetric reconstruction applied to perceptual studies of size and weight

We explore the application of volumetric reconstruction from structured-light sensors in cognitive neuroscience, specifically in the quantification of the size-weight illusion, whereby humans tend to systematically perceive smaller objects as heavier. We investigate the performance of two commercial structured-light scanning systems in comparison to one we developed specifically for this application. Our method has two main distinct features: First, it only samples a sparse series of viewpoints, unlike other systems such as the Kinect Fusion. Second, instead of building a distance field for the purpose of points-to-surface conversion directly, we pursue a first-order approach: the distance function is recovered from its gradient by a screened Poisson reconstruction, which is very resilient to noise and yet preserves high-frequency signal components. Our experiments show that the quality of metric reconstruction from structured light sensors is subject to systematic biases, and highlights the factors that influence it. Our main performance index rates estimates of volume (a proxy of size), for which we review a well-known formula applicable to incomplete meshes. Our code and data will be made publicly available upon completion of the anonymous review process.

Superior colliculus neuronal ensemble activity signals optimal rather than subjective confidence

Significance Previously, the neuronal correlates of perceptual confidence have been identified in neural circuits responsible for deciding what an animal sees. However, behaviorally, confidence and perceptual decision accuracy are confounded; we are usually more confident about perceptual decisions when they are accurate. To tease them apart, we introduced a task with stimulus conditions that produced similar decision accuracy but different reports of subjective confidence. We decoded decision performance from neuronal signals in nonhuman primates in a subcortical region involved in decision-making, the superior colliculus (SC), and found that SC ensemble activity tracks decision accuracy, but not subjective confidence. These results challenge current ideas about how to measure subjective confidence in experiments and inspire ways to study its neuronal mechanisms. Recent studies suggest that neurons in sensorimotor circuits involved in perceptual decision-making also play a role in decision confidence. In these studies, confidence is often considered to be an optimal readout of the probability that a decision is correct. However, the information leading to decision accuracy and the report of confidence often covaried, leaving open the possibility that there are actually two dissociable signal types in the brain: signals that correlate with decision accuracy (optimal confidence) and signals that correlate with subjects’ behavioral reports of confidence (subjective confidence). We recorded neuronal activity from a sensorimotor decision area, the superior colliculus (SC) of monkeys, while they performed two different tasks. In our first task, decision accuracy and confidence covaried, as in previous studies. In our second task, we implemented a motion discrimination task with stimuli that were matched for decision accuracy but produced different levels of confidence, as reflected by behavioral reports. We used a multivariate decoder to predict monkeys’ choices from neuronal population activity. As in previous studies on perceptual decision-making mechanisms, we found that neuronal decoding performance increased as decision accuracy increased. However, when decision accuracy was matched, performance of the decoder was similar between high and low subjective confidence conditions. These results show that the SC likely signals optimal decision confidence similar to previously reported cortical mechanisms, but is unlikely to play a critical role in subjective confidence. The results also motivate future investigations to determine where in the brain signals related to subjective confidence reside.

Does unconscious perception really exist? Continuing the ASSC20 debate.

In our ASSC20 symposium, “Does unconscious perception really exist?”, the four of us asked some difficult questions about the purported phenomenon of unconscious perception, disagreeing on a number of points. This disagreement reflected the objective of the symposium: not only to come together to discuss a single topic of keen interest to the ASSC community, but to do so in a way that would fairly and comprehensively represent the heterogeneity of ideas, opinions, and evidence that exists concerning this contentious topic. The crux of this controversy rests in no small part on disagreement about what is meant by the terms of the debate and how to determine empirically whether a state is unconscious or not. These are issues that directly concern all of us who study consciousness, so it seems it would be in our best interest to strive for consensus. Given the conversation at ASSC20, we are pleased to have the opportunity to address some of the nuanced topics that arose more formally, and share some of the thinking we have done since the meeting. To reflect the heterogeneity of ideas and opinions surrounding this topic, we have organized this discussion into four distinct contributions.

Perceptual confidence neglects decision-incongruent evidence in the brain

Our perceptual experiences are accompanied by a subjective sense of certainty. These confidence judgements typically correlate meaningfully with the probability that the relevant decision is correct1,2,3,4,5,6, bolstering prevailing opinion that both perceptual decisions and confidence optimally reflect the probability of having made a correct decision6,7,8,9,10,11,12,13. However, recent behavioural reports suggest that confidence computations overemphasize information supporting a decision, while selectively down-weighting evidence for other possible choices14,15,16,17,18,19. This view remains controversial, and supporting neurobiological evidence has been lacking. Here we use intracranial electrophysiological recordings in humans together with machine-learning techniques to demonstrate that perceptual decisions and confidence rely on spatiotemporally separable neural representations in a face/house discrimination task. We then use normative computational models to show that confidence relies excessively on evidence supporting a decision (for example, face evidence for a ‘face’ decision), even while decisions themselves reflect the optimal balance of all evidence (for example, both face and house evidence). Thus, confidence may not reflect a readout of the probability of being correct; instead, observers may sacrifice optimality in favour of self-consistency20 in the face of limited neural and computational resources. Although seemingly suboptimal, this strategy may reflect the inference problem that perceptual systems are evolutionarily optimized to solve.